This invention relates to a system specifically adapted for use in spectral geographic information systems. More particularly, this invention relates to a system configured to translate large information databases into a more compact database for analysis using various hyperspectral techniques to facilitate the location of targets.
Hyperspectral imagery consists of hundreds of “spectra,” or measurements of reflected or emitted energy. Hyperspectral sensors scan many channels across a relatively narrow bandwidth and provide detailed information about target spatial and spectral patterns. Absorption and emission bands of given substances often occur within very narrow bandwidths. They allow high-resolution, hyperspectral sensors to distinguish the properties of the substances to a finer degree than an ordinary broadband sensor. The intensity of this energy can be measured at various wavelengths. Many objects and substances have spectral characteristics that are unique and a unique spectral “signature” allows that object or substance to be identified through various spectral analyses. By using sensors to detect multiple wavelengths, it's possible to differentiate between natural and manmade objects—even different kinds of vegetation and various types of building materials.
The utility of subdividing the ultraviolet, visible and infrared spectra into distinct bins for imaging has long been known. In multispectral imaging (MSI), multiple images of a scene or object are created using light from different parts of the spectrum. If the proper wavelengths are selected, multispectral images can be used to detect many militarily important items such as camouflage, thermal emissions and hazardous wastes to name a few.
A primary goal of using multispectral/hyperspectral remote sensing image data is to discriminate, classify, identify as well as quantify materials present in the image. Another important application is subpixel target detection, which allows one to detect targets of interest with sizes smaller than the pixel resolution, and abundance estimation, which allows one to detect concentrations of different signature spectra present in pixels. In remote sensing image analysis, the difficulty arises in the fact that a scene pixel is mixed linearly or nonlinearly by different materials resident in the pixel where direct applications of commonly used image analysis techniques generally do not work well.
Hyperspectral imaging (HSI), is a passive technique (i.e., depends upon the sun or some other independent illumination source) that creates a large number of images from contiguous, rather than disjoint, regions of the spectrum, typically, with much finer resolution. This increases sampling of the spectrum and provides a great increase in information. Many remote sensing tasks which are impractical or impossible can be accomplished with HSI. For example, detection of chemical or biological weapons, bomb damage assessment of underground structures, and foliage penetration to detect troops and vehicles are just a few potential HSI missions.
Hyperspectral imaging technology uses hundreds of very narrow wavelength bands to “see” reflected energy from objects on the ground. This energy appears in the form of “spectral fingerprints” across the light spectrum; enables collection of much more detailed data; and produces a much higher spectral resolution of a scene than possible using other remote sensing technologies.
Once these fingerprints are detected, special algorithms—repetitive, problem-solving mathematical calculations—then assess them to differentiate various natural and manmade substances from one another. “Signature” libraries may also be used to identify specific materials—e.g., rooftops, parking lots, grass, or mud—by comparing a library's pre-existing reference catalogs with freshly taken hyperspectral images of the battlefield from space.
Image processing equipment then portrays the various types of terrain and objects upon it in different colors forming a “color cube,” each based on the wavelength of the reflected energy captured by the image. These colors are subsequently “translated” into maps that correspond to certain types of material or objects to detect or identify military targets such as a tank or a mobile missile launcher. Algorithms can also categorize types of terrain and vegetation (useful, for example, in counter-narcotic operations), detecting features such as disturbed soil, stressed vegetation, and whether the ground will support the movement of military vehicles.
Using this technology, theater commanders can use mobile ground stations to process in real-time information transmitted by the satellite, which will allow theater commanders to keep pace with rapidly changing conditions.
Due to the large amount of data associated with HSI technology, analyzing and displaying this data is difficult and processor intensive. Clearly, there is a need for a method that can convert the large amount of data associated with HIS technology to a compact and efficient file structure that will ease the analysis and presentation of the data.